A knowledge-driven pipeline for transforming big data into actionable knowledge

authored by

Maria Esther Vidal, Kemele M. Endris, Samaneh Jozashoori, Guillermo Palma

Abstract

Big biomedical data has grown exponentially during the last decades, as well as the applications that demand the understanding and discovery of the knowledge encoded in available big data. In order to address these requirements while scaling up to the dominant dimensions of big biomedical data –volume, variety, and veracity– novel data integration techniques need to be defined. In this paper, we devise a knowledge-driven approach that relies on Semantic Web technologies such as ontologies, mapping languages, linked data, to generate a knowledge graph that integrates big data. Furthermore, query processing and knowledge discovery methods are implemented on top of the knowledge graph for enabling exploration and pattern uncovering. We report on the results of applying the proposed knowledge-driven approach in the EU funded project iASiS (http://project-iasis.eu). in order to transform big data into actionable knowledge, paying thus the way for precision medicine and health policy making.

Organisation(s)

L3S Research Centre

External Organisation(s)

German National Library of Science and Technology (TIB)

Type

Conference contribution

Pages

44-49

No. of pages

6

Publication date

30.12.2018

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Theoretical Computer Science, Computer Science(all)

Sustainable Development Goals

SDG 3 - Good Health and Well-being

Electronic version(s)

https://doi.org/10.1007/978-3-030-06016-9_4 (Access: Closed)